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Liver fat surrogates: HSI and NAFLD Liver Fat Score

Source: R/liver_fat_markers.R
liver_fat_markers.Rd

Computes:

  • HSI = 8 * (ALT/AST) + BMI + 2 (if female) + 2 (if diabetes)

  • NAFLD-LFS = -2.89 + 1.18MetS + 0.45Type2DM + 0.15Insulin_u + 0.04AST - 0.94*(AST/ALT)

Usage

liver_fat_markers(
  data,
  col_map = NULL,
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  na_warn_prop = 0.2,
  verbose = TRUE
)

Arguments

data

Data frame with needed columns (see col_map).

col_map

Named list mapping:

  • Required for HSI: ALT, AST, BMI

  • Optional direct inputs: sex, diabetes, MetS, insulin

  • Optional to derive MetS or insulin: I0, waist, TG, HDL_c, sbp, bp_sys, bp_treated, glucose, G0

na_action

One of c("keep","omit","error","ignore","warn").

na_warn_prop

Proportion in \([0,1]\) for high-missingness warnings when na_action = "warn". Default 0.2.

verbose

Logical; if TRUE, prints column mapping and computing messages.

Value

A tibble with columns HSI and NAFLD_LFS.

Details

Assumptions/units:

  • ALT, AST in U/L; BMI in kg/m^2.

  • insulin is expected in muU/mL; if unavailable and I0 is provided, I0 is interpreted in pmol/L and converted to muU/mL via /6.

  • MetS is taken directly if provided; otherwise derived using a simplified NCEP-ATP III style rule when sufficient inputs exist.

  • Type2DM is taken from diabetes (logical or 0/1).

These scores are surrogate indices for research/feature-engineering use and are not validated as standalone clinical diagnostic tools.

References

Lee J, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, Kim YJ, Yoon J, Cho S, Sung M, Lee H (2010). “Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease.” Digestive and Liver Disease, 42(7), 503–508. doi:10.1016/j.dld.2009.08.002 . Kotronen A, Peltonen M, Hakkarainen A, Sevastianova K, Bergholm R, Johansson LM, Lundbom N, Rissanen A, Ridderstrale M, Groop L, Orho-Melander M, Yki-Järvinen H (2009). “Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors.” Gastroenterology, 137(3), 865–872. doi:10.1053/j.gastro.2009.06.005 .

Examples

df <- data.frame(ALT=20, AST=25, BMI=27, sex="female", diabetes=FALSE, I0=60)
liver_fat_markers(
  df,
  col_map = list(ALT="ALT", AST="AST", BMI="BMI",
                 sex="sex", diabetes="diabetes", I0="I0")
)
#> liver_fat_markers(): reading input 'df' — 1 rows × 6 variables
#> liver_fat_markers(): col_map (7 columns — 6 specified, 1 inferred from data)
#>   ALT               ->  'ALT'
#>   AST               ->  'AST'
#>   BMI               ->  'BMI'
#>   sex               ->  'sex'
#>   diabetes          ->  'diabetes'
#>   I0                ->  'I0'
#>   insulin           ->  'I0'    (inferred)
#> liver_fat_markers(): computing markers:
#>   HSI        [8*(ALT/AST) + BMI + sex + diabetes]
#>   NAFLD_LFS  [MetS/insulin/diabetes required; NA if unavailable]
#> liver_fat_markers(): results: HSI 1/1, NAFLD_LFS 0/1
#> # A tibble: 1 × 2
#>     HSI NAFLD_LFS
#>   <dbl>     <dbl>
#> 1  35.4        NA